Tech Term Decoded: Named Entity Recognition (NER)

Definition

Named entity recognition (NER), also known as entity chunking or entity extraction, is a field of natural language processing (NLP) that detects designated categories of objects in a body of text. These categories include among others names of individuals, organizations, locations, expressions of times, quantities, medical codes, monetary values and percentages, among others. Simply put, NER is the process of analyzing a piece of text (i.e., a sentence, paragraph or entire document), finding and classifying the entities that refer to each category [1].

For example;

Ebonyi is known for rice production (Location)

Ebonyi Angels dominated the women's league (Organization)

Dangote is Africa's richest man (Person)

Dangote Cement reported strong profits (Organization)

Named entity recognition process [2]

Origin

The concept of Named-Entity Recognition originated in the 1990s, within the broader field of computational linguistics and natural language processing. The first NER systems focused on identifying simple categories such as names of people, companies, and locations. Gradually, advancements in machine learning and deep learning, propelled the development of NER, leading to more sophisticated techniques for entity recognition and classification.

Also, over the course time, the development of annotated corpora and the rise of large-scale language models have greatly improved the accuracy and efficiency of NER systems, transforming NER from a rudimentary entity recognition approach to a sophisticated and adaptable technology, capable of handling complex linguistic tasks with precision [3].

Context and usage

The applications of NER cuts across several sectors, changing the way we extract and use information. Some of them are as follows:

• Research: Ner enables academics and researchers, to process large volumes of text, identifying mentions of specific entities related to their work. This leads to fast research process and ensures comprehensive data analysis.
• News aggregation: NER plays a key role in categorizing news articles based on the primary entities mentioned. This process helps readers to easily find stories about specific people, places, or organizations, streamlining the news consumption process.
• Legal document analysis: In the legal field, NER automates the process of going through lengthy documents to find relevant entities like names, dates, or locations, making legal research and analysis more efficient.
• Customer support:  Processing customer queries becomes more efficient with NER. Companies can quickly detect common issues related to specific products or services, ensuring that customer concerns are addressed promptly and effectively.

Why it Matters

According to MarketsandMarkets, the global NLP market size is expected to grow from $18.9 billion in 2023 to $68.1 billion by 2028, with NER being instrumental in this growth. Have you ever asked how a search engine pulls out exactly the right person, place, or company from a sea of words? Or how chatbots seem to understand which entities in your message are crucial? Named Entity Recognition (NER) is a key technology in Natural Language Processing (NLP) that enables machines to identify and categorize the essential pieces of text. From recognizing that "Ogun" is a state rather than the Yoruba deity to picking out critical terms in medical documents,

This significant growth highlights the increasing importance of Named Entity Recognition in harnessing the power of unstructured data across various industries. NER converts vast unstructured data into actionable insights. It’s estimated that unstructured data accounts for 80–90% of all data, making tools like NER indispensable for converting this information into meaningful patterns [4].

In Practice

A real-life case study of Named Entity Recognition (NER) been practiced can be seen in the case of Microsoft Azure AI. NER is one of the features offered by Azure AI Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. It can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities. The prebuilt NER feature has a preset list of recognized entities. The custom NER feature allows you to train the model to recognize specialized entities specific to your use case [5].

See Also

Related NLP and Text Processing terms:

• Natural Language Generation (NLG): AI capability to produce human-like text or speech from data or structured input
• Natural Language Processing (NLP): Field of AI focused on enabling computers to understand and work with human language
• Natural Language Understanding (NLU): AI capability to comprehend and interpret human language meaning and intent
• Part of Speech Tagging: Process of labeling words with their grammatical categories (noun, verb, adjective, etc.)
• Sentiment Analysis: Process of determining emotional tone or opinion expressed in text

 References

• IBM. (2023). What is named entity recognition?
• Slide Team. (2025). Named Entity Recognition NER Processes Natural Language Processing PPT PowerPoint ST AI SS 
• Lark Editorial Team. (2023). Named Entity Recognition Ner.
• Kanerika Inc. (2024). Named Entity Recognition: A Comprehensive Guide to NLP’s Key Technology.
• Microsoft. (2025). What is Named Entity Recognition (NER) in Azure AI Language?